Biological plausibility [1]}, [2]}, [3]}, [4]}, [5]} has also been emphasized in the machine learning community, with the hope to uncover efficient learning algorithms inspired by neuroscience. However, many proposed STDP-consistent algorithms assume symmetric weights, a constraint generally considered biologically implausible. For example, [4]} introduces a novel learning paradigm termed Equilibrium Propagation that offers a biologically plausible mechanism for backpropagation and credit assignment in Deep Learning. The learning algorithm implements a form of STDP for neural networks with symmetric weights, i.e. \(w_{ij} = w_{ji}\) , such that, in one of the learning phases, the weight update rule satisfies
\(\frac{\partial w_{ij}}{\partial t} \propto \rho (u_i) \frac{d\rho (u_j)}{dt}+\rho (u_j) \frac{d\rho (u_i)}{dt}\) , where \(u_i\)  and \(\rho (u_i)\)  are the membrane potential and its firing rate. In contrast, our model possesses a different weight update rule (see Spike-Timing-Dependent Plasticity section), does not pose the symmetric weight condition, and can reproduce asymmetric STDP-like curves (see the STDP and MPN Learning Rule section).
